Performance comparison of geometric and statistical methods for endmembers extraction in hyperspectral imagery

Spectral unmixing decomposes an hyperspectral image into a collection of reflectance spectra of the macroscopic materials present in the scene, called endmembers, and the corresponding abundance fractions of these constituents. The purpose of this paper is to compare the performance of several algorithms that process unsupervised endmember extraction from hyperspectral images in the visible and NIR spectral ranges. After giving an analytical formulation of the observations, two significantly different approaches have been described. The first one exploits convex geometry the problem answers to. The second one is based on statistical principles of Independent Component Analysis, which is a classical resolution of the Blind Source Separation issue. First, the performance of the algorithms are compared on synthetic images and sensibility to noise is studied. Then the best methods are applied on part of a HyMap image.